suppressMessages(library(Seurat))
suppressMessages(library(ggplot2))
suppressMessages(library(scater))
srt <- readRDS("/mnt/nmorais-nfs/marta/pB_joana/pC_data/srt-mono-macs-young-without-residents-bec.rds")
srt
## An object of class Seurat
## 16864 features across 12313 samples within 1 assay
## Active assay: RNA (16864 features, 3000 variable features)
## 3 dimensional reductions calculated: pca, umap, tsne
table(srt$cell_type,srt$sample)
##
## yg0 yg1 yg3 yg5
## CD11a Myeloid 89 7 9 6
## Infiltrating Type 1 293 1371 314 46
## Infiltrating Type 2 26 1865 46 12
## Macrophages intermediate 15 25 2784 2131
## Macrophages Repair 15 12 1254 1922
table(srt$sorting_day,srt$sample)
##
## yg0 yg1 yg3 yg5
## day1 0 3280 4407 0
## day2 438 0 0 4117
table(srt$lane,srt$sample)
##
## yg0 yg1 yg3 yg5
## lane1 0 3280 0 4117
## lane2 438 0 4407 0
table(srt$cell_type,srt$sorting_day)
##
## day1 day2
## CD11a Myeloid 16 95
## Infiltrating Type 1 1685 339
## Infiltrating Type 2 1911 38
## Macrophages intermediate 2809 2146
## Macrophages Repair 1266 1937
table(srt$cell_type,srt$lane)
##
## lane1 lane2
## CD11a Myeloid 13 98
## Infiltrating Type 1 1417 607
## Infiltrating Type 2 1877 72
## Macrophages intermediate 2156 2799
## Macrophages Repair 1934 1269
colors <- c("darkorange4","red", "yellow", "green4", "blue" )
DimPlot(srt, reduction = "tsne", group.by = "cell_type", cols = colors, pt.size = 0.5)
DimPlot(srt, reduction = "tsne", group.by = "sample", pt.size = 0.5)
DimPlot(srt, reduction = "tsne", group.by = "lane", cols = c("orange", "blue"), pt.size = 0.5)
DimPlot(srt, reduction = "tsne", group.by = "sorting_day", cols = c("orange", "blue"), pt.size = 0.5)
DimPlot(srt[,srt$lane == "lane1"], reduction = "tsne", group.by = "lane", cols = c("orange", "blue"), pt.size = 0.5)
DimPlot(srt[,srt$lane == "lane2"], reduction = "tsne", group.by = "lane", cols = c("blue"), pt.size = 0.5)
DimPlot(srt[,srt$sorting_day == "day1"], reduction = "tsne", group.by = "sorting_day", cols = c("orange", "blue"), pt.size = 0.5)
DimPlot(srt[,srt$sorting_day == "day2"], reduction = "tsne", group.by = "sorting_day", cols = c( "blue"), pt.size = 0.5)
DimPlot(srt, reduction = "umap", group.by = "cell_type", cols = colors, pt.size = 0.5)
DimPlot(srt, reduction = "umap", group.by = "sample", pt.size = 0.5)
DimPlot(srt, reduction = "umap", group.by = "lane", cols = c("orange", "blue"), pt.size = 0.5)
DimPlot(srt, reduction = "umap", group.by = "sorting_day", cols = c("orange", "blue"), pt.size = 0.5)
DimPlot(srt[,srt$lane == "lane1"], reduction = "umap", group.by = "lane", cols = c("orange", "blue"), pt.size = 0.5)
DimPlot(srt[,srt$lane == "lane2"], reduction = "umap", group.by = "lane", cols = c("blue"), pt.size = 0.5)
DimPlot(srt[,srt$sorting_day == "day1"], reduction = "umap", group.by = "sorting_day", cols = c("orange", "blue"), pt.size = 0.5)
DimPlot(srt[,srt$sorting_day == "day2"], reduction = "umap", group.by = "sorting_day", cols = c( "blue"), pt.size = 0.5)
DimPlot(srt[,srt$sample == "yg0"], reduction = "umap", group.by = "sample", cols = c( "tomato"), pt.size = 0.5)
DimPlot(srt[,srt$sample == "yg1"], reduction = "umap", group.by = "sample", cols = c( "green3"), pt.size = 0.5)
DimPlot(srt[,srt$sample == "yg3"], reduction = "umap", group.by = "sample", cols = c( "dodgerblue"), pt.size = 0.5)
DimPlot(srt[,srt$sample == "yg5"], reduction = "umap", group.by = "sample", cols = c( "purple"), pt.size = 0.5)
mat <- GetAssayData(srt, slot = "data")
df1 <- data.frame(cell_type = srt$cell_type,
sample = srt$sample,
sorting = srt$sorting_day,
lane = srt$lane)
varMatrix <- getVarianceExplained(mat, variables = df1)
plotExplanatoryVariables(
varMatrix,
variables = variables) +
ggtitle("Young - Mono/macs without resident") +
theme(text = element_text(size=20)) +
scale_color_manual(values=c( "dodgerblue" ,"purple" , "green3","orange", "blue", "red"))
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Warning in self$trans$transform(x): NaNs produced
## Warning: Transformation introduced infinite values in continuous x-axis
## Warning: Removed 197 rows containing non-finite values (`stat_density()`).
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.6 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] scater_1.22.0 scuttle_1.4.0
## [3] SingleCellExperiment_1.16.0 SummarizedExperiment_1.24.0
## [5] Biobase_2.54.0 GenomicRanges_1.46.1
## [7] GenomeInfoDb_1.30.1 IRanges_2.28.0
## [9] S4Vectors_0.32.4 BiocGenerics_0.40.0
## [11] MatrixGenerics_1.6.0 matrixStats_0.63.0
## [13] ggplot2_3.4.1 SeuratObject_4.1.3
## [15] Seurat_4.1.1
##
## loaded via a namespace (and not attached):
## [1] plyr_1.8.8 igraph_1.3.5
## [3] lazyeval_0.2.2 sp_1.6-0
## [5] splines_4.1.2 BiocParallel_1.28.3
## [7] listenv_0.9.0 scattermore_0.8
## [9] digest_0.6.31 htmltools_0.5.4
## [11] viridis_0.6.2 fansi_1.0.4
## [13] magrittr_2.0.3 ScaledMatrix_1.2.0
## [15] tensor_1.5 cluster_2.1.4
## [17] ROCR_1.0-11 globals_0.16.2
## [19] spatstat.sparse_3.0-0 colorspace_2.1-0
## [21] ggrepel_0.9.3 xfun_0.37
## [23] dplyr_1.1.1 RCurl_1.98-1.10
## [25] jsonlite_1.8.4 progressr_0.13.0
## [27] spatstat.data_3.0-0 survival_3.5-0
## [29] zoo_1.8-11 glue_1.6.2
## [31] polyclip_1.10-4 gtable_0.3.3
## [33] zlibbioc_1.40.0 XVector_0.34.0
## [35] leiden_0.4.3 DelayedArray_0.20.0
## [37] BiocSingular_1.10.0 future.apply_1.10.0
## [39] abind_1.4-5 scales_1.2.1
## [41] spatstat.random_3.1-3 miniUI_0.1.1.1
## [43] Rcpp_1.0.10 viridisLite_0.4.1
## [45] xtable_1.8-4 reticulate_1.26
## [47] spatstat.core_2.4-4 rsvd_1.0.5
## [49] htmlwidgets_1.6.1 httr_1.4.4
## [51] RColorBrewer_1.1-3 ellipsis_0.3.2
## [53] ica_1.0-3 farver_2.1.1
## [55] pkgconfig_2.0.3 sass_0.4.5
## [57] uwot_0.1.14 deldir_1.0-6
## [59] utf8_1.2.3 labeling_0.4.2
## [61] tidyselect_1.2.0 rlang_1.1.0
## [63] reshape2_1.4.4 later_1.3.0
## [65] munsell_0.5.0 tools_4.1.2
## [67] cachem_1.0.6 cli_3.6.1
## [69] generics_0.1.3 ggridges_0.5.4
## [71] evaluate_0.20 stringr_1.5.0
## [73] fastmap_1.1.0 yaml_2.3.7
## [75] goftest_1.2-3 knitr_1.42
## [77] fitdistrplus_1.1-8 purrr_1.0.1
## [79] RANN_2.6.1 pbapply_1.7-0
## [81] future_1.31.0 nlme_3.1-162
## [83] sparseMatrixStats_1.6.0 mime_0.12
## [85] compiler_4.1.2 rstudioapi_0.14
## [87] beeswarm_0.4.0 plotly_4.10.1
## [89] png_0.1-8 spatstat.utils_3.0-1
## [91] tibble_3.2.1 bslib_0.4.2
## [93] stringi_1.7.12 highr_0.10
## [95] lattice_0.20-45 Matrix_1.5-3
## [97] vctrs_0.6.1 pillar_1.9.0
## [99] lifecycle_1.0.3 spatstat.geom_3.0-6
## [101] lmtest_0.9-40 jquerylib_0.1.4
## [103] BiocNeighbors_1.12.0 RcppAnnoy_0.0.20
## [105] data.table_1.14.8 cowplot_1.1.1
## [107] bitops_1.0-7 irlba_2.3.5.1
## [109] httpuv_1.6.8 patchwork_1.1.2
## [111] R6_2.5.1 promises_1.2.0.1
## [113] KernSmooth_2.23-20 gridExtra_2.3
## [115] vipor_0.4.5 parallelly_1.34.0
## [117] codetools_0.2-18 MASS_7.3-58.2
## [119] withr_2.5.0 sctransform_0.3.5
## [121] GenomeInfoDbData_1.2.7 mgcv_1.8-41
## [123] parallel_4.1.2 beachmat_2.10.0
## [125] grid_4.1.2 rpart_4.1.19
## [127] tidyr_1.3.0 rmarkdown_2.20
## [129] DelayedMatrixStats_1.16.0 Rtsne_0.16
## [131] shiny_1.7.4 ggbeeswarm_0.7.1